Senior Data Scientist
Aditya Birla Capital · Mumbai
- Experience7–11 yrs
- SalaryNot disclosed
- Work modeonsite
- Levelexecutive
- Posted3 Sept 2026
About Aditya Birla Capital
Aditya Birla Capital is hiring in Mumbai in financial services. This role looks for around 7+ years of experience.
Skills
- Health Insurance
- machine learning
- XGBoost
- Random Forest
- Neural Networks
- Deep Learning
- claims analytics
- underwriting
- fraud detection
- risk modeling
- Python
- SQL
- scikit-learn
- end-to-end model deployment
- MLOps
- structured datasets
- unstructured datasets
The role
A data scientist at a financial services company develops health insurance machine learning solutions for claims prediction, fraud detection, and risk modeling, applying Python, XGBoost, and MLOps across the model lifecycle. The role also uses predictive analytics and TensorFlow to deliver scalable, production-ready decision systems.
Full job description
The role focuses on designing, developing, and deploying advanced data science and AI solutions in the Health Insurance domain. The incumbent will translate business requirements into scalable machine learning solutions, ensure high data quality, and drive measurable business impact through predictive analytics and AI-driven decision-making.
The core responsibilities for the job include the following:
Advanced Data Science and AI Development:
Build and deploy machine learning models such as XGBoost, ANN, and deep learning models for health insurance use cases.
Work on predictive analytics problems, including claims prediction, fraud detection, underwriting, pricing, and risk modeling.
Improve model performance using feature engineering, hyperparameter tuning, and algorithm optimization.
End-to-End Model Lifecycle Ownership:
Manage the complete ML lifecycle, including data extraction, preprocessing, model building, validation, deployment, and monitoring.
Ensure production-grade deployment in collaboration with engineering teams.
Maintain model versioning, documentation, and reproducibility standards.
Experimentation and Continuous Improvement:
Conduct A/B testing, statistical analysis, and post-deployment monitoring.
Identify model gaps and continuously improve performance based on business feedback.
Drive innovation by exploring new algorithms, techniques, and AI methodologies.
Stakeholder and Business Collaboration:
Work closely with Health Insurance business stakeholders to understand requirements and translate them into analytical solutions.
Align AI/ML solutions with key business KPIs such as claims accuracy, fraud reduction, and operational efficiency.
Communicate insights and model outputs in a clear, business-friendly manner.
Technical Leadership and Best Practices:
Establish and enforce best practices in coding, model development, and documentation.
Mentor junior data scientists and support capability building within the team.
Stay updated with emerging AI/ML trends and ensure adoption of relevant technologies.
Scalable AI Solutions:
Build reusable ML pipelines and scalable frameworks for multiple health insurance use cases.
Ensure solutions are production-ready, maintainable, and scalable across business functions.
Promote standardization and knowledge sharing across teams.
Requirements:
Strong experience in Health Insurance domain (mandatory).
Expertise in machine learning algorithms (XGBoost, Random Forest, Neural Networks, Deep Learning, etc. ).
Strong understanding of claims analytics, underwriting, fraud detection, and risk modeling.
Hands-on experience with Python, SQL, and ML libraries (Scikit-learn and TensorFlow/PyTorch preferred).
Experience in end-to-end model deployment and MLOps practices.
Strong analytical thinking and problem-solving skills.
Experience in working with large-scale structured and unstructured datasets.
Good communication skills for stakeholder interaction.